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Chinese Automation Congress (CAC)

作者： Yisong Zhang Guoxing Yi Hao Wang Yu Cheng Yiran Chen Zhennan Wei Space Control and Inertial Technology Research Center Harbin Institute of Technology Harbin China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The target assignment in multi-unmanned aerial vehicle (multi-UAV) cooperative reconnaissance is a classic problem in weapon-target assignment. Despite the significance of the problem, most of the existing algorithms can't meet the application requirements in solution quality and computational efficiency. Therefore, a self-learning artificial bee colony (ABC) algorithm based on deep reinforcement learning (DRL) is proposed in this study to solve the target assignment problem in multi-UAV cooperative reconnaissance (named DRLABC). In DRLABC, the search equation for the employed bee phase is intelligently adjusted by proximal policy optimization (PPO). Moreover, an improved strategy is adopted for the onlooker bee phase to enhance the comprehensive performance of the algorithm. The learning performance and effectiveness of DRLABC are compared with other rival algorithms using multiple simulation instances with different problem scales. Experimental results show that the proposed algorithm significantly outperforms its competitors in solving target assignment problems.

关键词： Automation Heuristic algorithms Weapons Reconnaissance artificial bee colony algorithm Deep reinforcement learning Mathematical models Computational efficiency Optimization

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Optimization of Production Scheduling for Turbocharger Manufacturing Reprocessing

Optimization of Production Scheduling for Turbocharger Manuf...

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Energy and Electrical Engineering (EEE), International Conference on

作者： Yi Li Yuhang Zhou Yijia Wang School of Mechanical and Electrical Engineering Wuhan University of Technology Wuhan China

ISBN: (数字)9798350341768

ISBN: (纸本)9798350341775

In order to improve the production efficiency of W enterprise, the turbocharger casting post-processing production line of the enterprise is analyzed and improved, so as to reduce the production cost of the product and improve the economic efficiency of the enterprise. First of all, This paper collected the product and production line information of W enterprise through actual research, analyzed the existing problems of turbocharger casting post-processing production line, researched the turbocharger casting post-processing production scheduling problem, determined that the production line production scheduling problem is the replacement of the water flow workshop scheduling problem, and set up a mathematical model of workshop scheduling optimization for this problem. Finally, the optimization model is solved based on the artificial bee colony algorithm, the simulation results obtained are analyzed, and the adaptability of the artificial bee colony algorithm in solving this type of problem is summarized.

关键词： Casting Job shop scheduling Conferences Simulation Optimization models Production artificial bee colony algorithm Linear programming Manufacturing Optimization

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Cloud Computing Resource Scheduling Based on Modified bee colony algorithm

Cloud Computing Resource Scheduling Based on Modified Bee Co...

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Chinese Control and Decision Conference, CCDC

作者： Shasha Zhao Huanwen Yan Qifeng Lin Yuyuan Lin Dengyin Zhang School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China Tongding Interconnection Information Co. Ltd. Suzhou China School of Internet of Things Tongda College of Nanjing University of Posts and Telecommunications Yangzhou China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

As cloud computing and the big data industry continue to expand, the demand for faster data processing speed is also on the rise. This has led to the emergence of a distributed processing resource scheduling system. However, resource scheduling faces major challenges such as resource allocation issues, excessively long processing times, high costs, and uneven workload distribution. In response to these challenges, this article proposes an enhanced approach using the Modified artificial bee colony algorithm (MABC). This algorithm incorporates inertia weights and global optimization components, expanding the algorithm's search range and enhancing its stability. By effectively addressing the difficulties in resource scheduling, it achieves multi-objective optimization results.

关键词： Industries Cloud computing Distributed processing Job shop scheduling Processor scheduling artificial bee colony algorithm Resource management

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Multiple Phase Change Material-Based Heat Sink for Cooling of Electronics: A Combined Experimental and Numerical Study

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ASME JOURNAL OF HEAT AND MASS TRANSFER 2023年第4期145卷 043001-043001页

作者： Marri, Girish Kumar Srikanth, R. Balaji, C. IIT Madras Dept Mech Engn Chennai 600036 India SUNY Binghamton Dept Mech Engn Binghamton NY 13905 USA

This paper reports an investigation of the thermal performance of an energy storage heat sink incorporated with multiple phase change materials (PCMs). A six-cavity cylindrical heat sink heated at the base is chosen for the investigations with Docosane, n-Eicosane, and Tetracosane as candidate PCMs. The phase transition of PCMs has been visualized with a digital camera and three-dimensional numerical simulations. The results show that the latent heat exploitation process of PCMs in a heat sink with multiple PCMs is different from the single PCM heat sink, where the PCMs in all cavities melt distinctly rather at a time, thereby opening up windows for obtaining deeper insights that can lead to better performing heat sinks. A trained artificial neural network (ANN) with 78 representative heat sink configurations based on the arrangement of the PCMs in the cavities as input and charging and discharging times as output is used to swiftly drive the optimization engine. Finally, multi-objective optimization is performed using the artificial bee colony algorithm with simultaneous consideration of two conflicting objectives (i.e., maximizing charging cycle time and minimizing discharging cycle time) of the heat sink. From the optimization study, best performing nondominated Pareto optimal heat sink configurations are obtained and validated with the in-house experimental results. From the investigations, it is found that the heat sink configurations with multiple PCMs perform on par with the single PCMs in the charging process and show a superiority of up to 24% in discharging process over a heat sink with single PCMs in terms of time to reach set point temperature.

关键词： artificial bee colony algorithm electronic cooling multi-objective optimization multiple PCMs melting and solidification

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Stochastic Framework for Smart Home Energy Management: Integrating Demand Response and Renewable Resources Using Improved ABC algorithm

Stochastic Framework for Smart Home Energy Management: Integ...

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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Mehran Haghighi Mohammad Reza Masoudi Hamid Karimi Hamid Reza Sezavar Department of Electrical Engineering National University of Skills (NUS) Tehran Iran Shiraz Electrical Power Distribution Company Shiraz Iran Department of Electrical and Computer Engineering Qom University of Technology Qom Iran

ISBN: (数字)9798331525132

ISBN: (纸本)9798331525149

This paper presents a home energy management system that optimizes the electricity demand of home appliances according to demand response programs. This home is equipped with renewable energy, including wind turbines and solar panels. The uncertainty of renewable energy is modeled using stochastic programming. Different types of loads have been used in this home, and the mentioned loads have been properly adjusted and distributed to minimize the cost of energy and deal with fluctuations in renewable energy. An improved artificial bee colony algorithm has been used to solve this problem. The results confirm that the introduced model can efficiently harvest all possible energy of the wind-solar system, manage uncertainty, minimize cost, and operate independently of the grid. These goals are achieved through the complex coordination of load management strategies in the proposed load response framework, which leads to an efficient and flexible energy management system. The numerical results reported in this paper show the effectiveness of the proposed method. The proposed plan is generally usable and valuable for any other type of smart home.

关键词： Renewable energy sources Costs Uncertainty Stochastic processes Smart homes Programming artificial bee colony algorithm Demand response Energy management systems Load modeling

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Attribute reduction on real-valued data in rough set theory using hybrid artificial bee colony: extended FTSBPSD algorithm

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SOFT COMPUTING 2017年第24期21卷 7543-7569页

作者： Chebrolu, Srilatha Sanjeevi, Sriram G. Natl Inst Technol Warangal Dept Comp Sci & Engn Warangal 506004 Andhra Pradesh India

Discretization and attribute reduction are two preprocessing steps for most of the induction algorithms. Discretization before attribute reduction will result in high computation cost as many irrelevant and redundant attributes need to be discretized. Attribute reduction before discretization may result in over-fitting of the data leading to low performance of the induction algorithm. In this paper, we have proposed a hybrid algorithm using artificial bee colony (ABC) algorithm and extended forward tentative selection with backward propagation of selection decision (EFTSBPSD) algorithm for attribute reduction on real-valued data in rough set theory (RST). Based on the principle of indiscernibility, the hybrid ABC-EFTSBPSD algorithm performs discretization and attribute reduction together. The hybrid ABC-EFTSBPSD algorithm takes as input the decision system consisting of real-valued attributes and determines a near optimal set of irreducible cuts. Here, optimality of the set of irreducible cuts is defined in terms of the cardinality of the set of irreducible cuts. Reduct is obtained from the determined approximate optimal set of irreducible cuts by extracting the attributes corresponding to the cuts in the obtained set of irreducible cuts. The proposed hybrid algorithm is tested on various data sets from University of California Machine Learning Repository. Experimental results obtained by the proposed hybrid algorithm are compared with those obtained by the Q-MDRA, ACO-RST and IMCVR algorithms described in the literature and found to give better classification accuracy when tested using (1) C4.5 classifier and (2) SVM classifier. The proposed hybrid algorithm has also shown reduced length of the reduct in comparison with the results obtained by Q-MDRA, ACO-RST and IMCVR algorithms.

关键词： Rough set theory Indiscernibility Boolean reasoning Discretization Attribute reduction artificial bee colony algorithm FTSBPSD algorithm

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ABCVS: An artificial bee colony for Generating Variable T-Way Test Sets

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第4期10卷 259-274页

作者： Alazzawi, Ammar K. Rais, Helmi Basri, Shuib Univ Teknol Petronas Fac Sci & Informat Technol Dept Comp & Informat Sci Bandar Seri Iskandar 32610 Perak Malaysia

To achieve acceptable quality and performance of any software product, it is crucial to assess various software components in the application. There exist various software-testing techniques such as combinatorial testing and covering array. However, problems such as t-way combinatorial explosion is still challenging in any combinatorial testing strategy, as it takes into consideration the entire combinations of input variables. Therefore, to overcome this problem, several optimizations and metaheuristic strategies have been suggested. One of the most effective optimization algorithms based techniques is the artificial bee colony (ABC) algorithm. This paper presents t-way generation strategy for both a uniform and variable strength test suite by applying the ABC strategy (ABCVS) to reduce the size of the test suite and to subsequently enhance the test suite generation interaction. To assess both the effectiveness and performance of the presented ABCVS, several experiments were conducted applying various sets of benchmarks. The results revealed that the proposed ABCVS outweigh the existing based strategies and demonstrated wider interaction between components as opposed to AI-search based and computational based strategies. The results also revealed higher prospect of ABCVS in the aspect of its effectiveness and performance as observed in the majority of case studies.

关键词： T-way testing variable-strength interaction combinatorial testing covering array test suite generation artificial bee colony algorithm

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Bank Loan Prediction System By Using Machine Learning

Bank Loan Prediction System By Using Machine Learning

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Aishwarya Andhale Om Bansode Rohan Chavan Jayashri Bagade Information Technology BRACT’s Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

In the banking and financial industry, the loan approval process is crucial and affects both lending institutions and loan applicants. This procedure has always been manual, depending on the knowledge of loan officers to evaluate different aspects and make wise judgments. On the other hand, manual processing takes time, is prone to bias, and can result in inefficiencies in the lending process. By enabling automation and optimization, the use of machine learning techniques has changed the loan approval process in recent years. This work presents the integration of the artificial bee colony (ABC) method with Random Forest to further improve prediction accuracy, in addition to evaluating SVM and Random Forest. The population-based optimization algorithm ABC was inspired by honeybee foraging techniques. In order to improve the Random Forest model’s ability to predict loan approval status, ABC iteratively explores the solution space and updates candidate solutions based on their fitness. This process aims to identify the ideal set of hyperparameters for the Random Forest model.

关键词： Support vector machines Machine learning algorithms Accuracy Manuals Prediction algorithms artificial bee colony algorithm Space exploration Classification algorithms Random forests Optimization

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A Study on the Economic Optimal Control Strategy of AC-DC Hybrid Microgrid Based on Consistency Theory

A Study on the Economic Optimal Control Strategy of AC-DC Hy...

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Power System and New Energy Technology Innovation Forum (NPSIF), Boao New Power System International Forum -

作者： Zhiyong Liu Qing Zeng Zhaojie Zeng Peipei Liu Guangdong Grid Corporation Shaoguan Electric Power Supply Bureau Shaoguan China

ISBN: (数字)9798331504557

ISBN: (纸本)9798331504564

In recent years, renewable energy has been developing rapidly, accompanied by the wide application of distributed generation. AC-DC hybrid microgrid, as a flexible and efficient energy system, is an important and key part of the construction of a new power system. This paper conducts an in-depth study on the economic optimization control strategy of AC-DC hybrid microgrid, aiming at improving the overall operation efficiency and economy performance of the new power system. Firstly, the control mathematical model of AC-DC hybrid microgrid is established, and the output characteristics and load characteristics of different power sources are considered. Secondly, this study proposes an active power control strategy based on equal incremental rate consistency algorithm and an economic scheduling control model considering frequency recovery for microgrid networks, thus realizing the construction of power regulation and control model of microgrid system. Through the improved artificial bee colony algorithm based on Gaussian variation and chaos perturbation, the economic optimization scheduling model is solved and run, and the effectiveness and feasibility of the control strategy is verified by simulation cases. The research results show its capability to significantly reduce the operating cost and improve the utilization rate of renewable energy on the premise of ensuring the stable operation of the system, and provide theoretical basis and technical support for the practical application of AC-DC hybrid microgrid.

关键词： Renewable energy sources Microgrids Power system stability artificial bee colony algorithm Hybrid power systems Scheduling Mathematical models Optimization Convergence Load modeling

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Optimized Deployment Technology for Heterogeneous Multi-base Sonar System Based on The IHS-ABC algorithm

Optimized Deployment Technology for Heterogeneous Multi-base...

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IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Xueli Sheng Manxin Liu Lei Tang Guoliang Ji Yan Wang Hongbo Miao National Key Laboratory of Underwater Acoustic Technology Key Laboratory for Polar Acoustics and Application of Ministry of Education Harbin Engineering University College of Underwater Acoustic Engineering Harbin Engineering University Harbin China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

In addressing the optimization deployment issues of heterogeneous multi-base sonar systems, this paper establishes a detection performance evaluation model for heterogeneous multi-base sonar systems in complex underwater environments and proposes a hybrid algorithm combining improved harmony search (IHS) and artificial bee colony (ABC) algorithm called IHS-ABC. This algorithm draws from the concepts of the IHS algorithm, constructs a source memory, employs a full-dimensional probability selection strategy, and modifies the scouting mechanism. The precision and convergence speed of the algorithm are validated using classic benchmark functions. The algorithm is applied to optimize the deployment of heterogeneous multi-base sonar systems in various marine environments under the criterion of maximum coverage. Simulation results indicate that the optimized detection coverage significantly increased, achieving the goal of optimized deployment and enhancing detection performance.

关键词： Performance evaluation Simulation Employment Signal processing algorithms Benchmark testing Signal processing artificial bee colony algorithm Optimization Sonar detection Convergence

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